A group of investigators with complementary expertise in molecular biology, structural biology, statistical physics, control theory, computer modeling, and computer science, propose to develop computational models for complex systems involved in the regulation of gene expression. Two initial research projects are proposed: Project I will focus on the structural and mechanistic basis of the first, and most highly regulated, step in gene expression: i.e., transcription. A combination of high-resolution structural methods, biophysical and biochemical methods, and molecular modeling will be used to construct structural models of the nanometer-scale supramolecular assembles involved in transcription initiation, elongation, and regulation. Computational-chemistry methods will be used to infer equilibrium and dynamic properties of assemblies, and statistical-mechanical methods will be used to incorporate information about all structural and reaction-state microstates important for transcription initiation, elongation, and regulation. Computational-chemistry methods will be used to infer equilibrium and dynamic properties of assemblies, and statistical- mechanical methods will be used to incorporate information about all structural and reaction-state microstates microstates important for transcription initiation, elongation, and regulation. Small-molecule inhibitors of protein-DNA interactions occurring in individual structural microstates will be designed, synthesized, and characterized. Project II, which will be tightly integrated with Project I, will focus on comprehensive quantitative simulation of two model biological regulatory networks: i.e., regulation of lactose and galactose assimilation in bacteria, and regulation of lytic and lysogenic developmental pathways in bacteriophage lambda. For each regulatory network, a multi-step analysis will be performed, with the first step involving simulation of the central circuitry of the regulatory network, and with successive steps involving simulation of first step involving simulation of the central circuitry of the regulatory network, and with successive steps involving simulation of sensory components that mediate transfer of information among he central circuitry, the cell, and the cellular environment. Inputs for simulations will include structural and mechanistic information from Project I, and quantitative data from systematic population and single-cell measurements of RNA levels, protein levels, small-molecule-effector levels, promoter activities, and protease activities. Simulations will be performed using direct, reverse-engineering, and hybrid methods. Simulations will be tested by comparing predicted and observed effects of perturbations of regulatory networks. The results to be obtained will contribute to understanding transcriptional regulation, will contribute development of approaches to simulate complex biological regulatory networks, and will contribute to development of approaches to predict effects of small-molecule agents on complex biological systems. The organizational infrastructure of the effort will be closely affiliated with the Rutgers University Initiative for Research and Education at the Biological/Mathematical/Physical-Sciences Interface (BioMaPS), which provides for establishment of a graduate courses, summer research internships, and seminars at the BioMaPS interface, and for recruitment of additional faculty members in biological computing and modeling.